As concurrent works, [1]} utilizes transformers and proposes to use a cycle-consistent attention mechanism to refine the feature maps to be more discriminative, without considering aggregation of matching scores. [2]} propose global and local enhancement module to refine the features using transformers and convolutions in the decoder, respectively. [3]} focuses solely on the transformer-based classifier by freezing the encoder and decoder, and adapting only the classifier to the task. Unlike them, we take different approach, focusing on aggregation of the high dimensional correlation maps in a novel and efficient way.
